Single-cell RNA sequencing (scRNA-seq) provides an effective tool to investigate the transcriptomic characteristics at the single-cell resolution. Due to the low amounts of transcripts in single cells and the technical biases in experiments, the raw scRNA-seq data usually includes large noise and makes the downstream analyses complicated. Although many methods have been proposed to impute the noisy scRNA-seq data in recent years, few of them take into account the prior associations across genes in imputation and integrate multiple types of imputation data to identify cell types. We present a new framework, NetImpute, towards the identification of cell types from scRNA-seq data by integrating multiple types of biological networks. We employ a statistic method to detect the noise data items in scRNA-seq data and develop a new imputation model to estimate the real values of data noise by integrating the PPI network and gene pathways. Meanwhile, based on the data imputed by multiple types of biological networks, we propose an integrated approach to identify cell types from scRNA-seq data. Comprehensive experiments demonstrate that the proposed network-based imputation model can estimate the real values of noise data items accurately and integrating the imputation data based on multiple types of biological networks can improve the identification of cell types from scRNA-seq data. Incorporating the prior gene associations in biological networks can potentially help to improve the imputation of noisy scRNA-seq data and integrating multiple types of network-based imputation data can enhance the identification of cell types. The proposed NetImpute provides an open framework for incorporating multiple types of biological network data to identify cell types from scRNA-seq data.

中文翻译：

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一种灵活的基于网络的插补融合方法，可从单细胞RNA序列数据中识别细胞类型。

单细胞RNA测序（scRNA-seq）提供了一种有效的工具，可以以单细胞分辨率研究转录组特征。由于单个细胞中的转录物数量较少，并且实验中存在技术偏见，因此原始的scRNA-seq数据通常会包含较大的噪音，并使下游分析变得复杂。尽管近年来提出了许多方法来估算嘈杂的scRNA-seq数据，但很少有人考虑到插补中各个基因之间的先验关联，并整合多种类型的插补数据以识别细胞类型。我们提出了一个新的框架NetImpute，旨在通过整合多种类型的生物网络从scRNA-seq数据中识别细胞类型。我们采用一种统计方法来检测scRNA-seq数据中的噪声数据项，并通过整合PPI网络和基因途径开发一种新的归因模型来估算数据噪声的实际值。同时，基于由多种类型的生物网络估算的数据，我们提出了一种从scRNA-seq数据识别细胞类型的集成方法。综合实验表明，所提出的基于网络的归因模型可以准确估计噪声数据项的真实值，并且基于多种类型的生物网络整合归因数据可以提高从scRNA-seq数据中识别细胞类型的能力。将先前的基因关联整合到生物网络中可以潜在地帮助改善嘈杂的scRNA-seq数据的归因，而整合多种类型的基于网络的归因数据可以增强对细胞类型的识别。提议的NetImpute提供了一个开放框架，用于合并多种类型的生物网络数据以从scRNA-seq数据中识别细胞类型。